Sleep is essential for physical and mental health. However, the high prevalence of sleep disorders is becoming a serious public health problem today, affecting 30–50% of the global population. In addition, sleep disorders and poor sleep quality have been associated with many chronic diseases, including diabetes, hypertension, depression, and obesity (1). Over the past few decades, a large body of scientific work has investigated the potential association between sleep duration and obesity in childhood. The findings suggest that sleep deprivation is associated with a significant increase in caloric intake (2). There is substantial evidence that dietary factors and obesity significantly influence the prevalence of sleep disorders and that LCDs and effective weight loss can significantly benefit people (3).

Obesity is usually defined as excessive fat storage, and obesity has significant health implications (4). BMI was first developed by a Belgian mathematician 200 years ago and is currently the most widely used tool for diagnosing obesity (5). Body mass index (BMI) is a common measure of obesity; however, BMI is not sufficient to assess obesity because people with the same BMI may have different body types and fat distribution (6). In addition, BMI does not distinguish abdominal fat distribution well. Researchers have developed a new indicator of obesity to more precisely assess fat mass, RFM, which includes waist circumference (WC) and height (7). RFM is more accurate than BMI in determining a person’s body fat percentage. RFM has been reported to be associated with several diseases, including venous thromboembolism, coronary heart disease, hypertension, and type 2 diabetes mellitus (8, 9). The recent study reported a stronger association between RFM and obesity compared to BMI and WC, which remained robust even when considering the influence of depression (10).

When it comes to the impact of obesity, available research shows that the prevalence of obesity has increased significantly worldwide over the past decade. A smaller but equally important increase has been observed in obesity-related comorbidities (type 2 diabetes, cardiovascular disease, cancer), which are becoming a real burden on healthcare systems. In addition to the most common complications of obesity, sleep disorders have recently become one of the obesity-related complications (11). In their study, Hargens et al. (12) pointed out that another critical mechanism linking obesity to sleep disorders is diet quality, which possibly acts through the effects of nutrients on inflammatory and/or hormonal responses that involve hunger-satiety mechanisms, energy metabolism, and circadian rhythms. A high-fat/high-carbohydrate diet has been reported to cause obesity and if untreated leads to steatohepatitis (13). In turn, increasing evidence has shown that sleep disorders may increase the risk of obesity and metabolic diseases (14).

Recently, the LCD score, characterized by lower carbohydrate intake and higher protein and fat intake, has been proposed (15). The LCD score is gaining widespread attention as an effective modality for weight loss and obesity prevention (15, 16). Previous studies have shown that LCD scores are associated with many aspects of health, such as diabetes, progression of coronary artery calcification, and psychological disorders (15, 17). In their study, Sangsefidi et al. reported that LCD scores may be associated with lower odds of metabolic syndrome in Iranian adults, according to a cross-sectional study (18). Lower low-carb diets have been demonstrated in recent research to improve weight loss, cardiovascular health, and mortality risk in middle-aged and older persons (19).

However, to the best of our knowledge, only a few studies have explored the association of LCD scores with RFM index and sleep disorders in the U.S. population. Herein, we revisited the relationship between RFM and LCD scores and sleep disorders, which may contribute to developing future approaches for preventing or treating sleep disorders. We investigated the association of RFM and LCD scores with sleep disorders by analyzing data from 5,394 participants in NHANES from 2005 to 2014.

The primary objective of this study was to assess the correlation between RFM and LCD scores and sleep disorders. We used NHANES data from 2005 to 2014 to investigate the relationship between RFM and LCD scores and sleep disorders in the U.S. national population. Our results showed that RFM and LCD score levels differed significantly between patients with sleep disorders and the normal population and that there was a positive correlation between RFM and LCD scores and sleep disorders. Univariate and multifactorial linear regression analyses showed that RFM and LCD scores were significantly and positively correlated with each other, which could explain why obese populations were more inclined to high-carbohydrate diets that can also cause obesity. Subsequent univariate and multivariate logistic regression analyses revealed that RFM and LCD scores may be independent risk factors for sleep disorders, and this correlation remained significant after adjusting for age, gender, race, marital status, education, annual household income, and smoking and drinking status. Subgroup analyses further showed that the correlation between RFM and LCD score and sleep disorders was stable regardless of age, gender, marital status, education, annual household income, and smoking and drinking status. In addition, there was no nonlinear association between RFM and LCD scores and sleep disorders analyzed by RCS.

The pathogenesis of sleep disorders is complex, and understanding the risk factors is necessary for effective prevention and treatment. Previous reports have identified obesity as one of the most important risk factors for sleep disorders, where a 6-unit increase in BMI resulted in a 4-fold increased risk of obstructive sleep apnea, which is characterized by recurrent narrowing and closure of the upper airway and can lead to intermittent hemoglobin oxygen desaturation, sleep fragmentation and daytime sleepiness (20–22). Obstructive sleep apnea syndrome (OSAS) is a highly prevalent sleep-related breathing disorder. It is characterized by hypopnea and apnea in ventilation, which cause intermittent hypoxia (IH) and lead to a number of other physiological effects. These include blood hypoxemia, hypercapnia, fragmented sleep, recurrent nocturnal arousals, enhanced respiratory effort, and increased sympathetic nerve activity (23). The risk of obstructive sleep apnoea syndrome (OSAS) is positively correlated with body mass index (BMI). As BMI increases, so too does the risk of OSAS, which is likely related to upper airway narrowing caused by excess fat tissue. Obesity can also induce a decrease in vital capacity, an imbalance in the ventilation-perfusion ratio, and limitations of lung and chest wall movement. Consequently, the countries with the highest prevalence of OSAS are those with high rates of obesity, and thus, the incidence of OSAS increases with increasing levels of obesity (24). However, while BMI is a commonly used indicator of obesity, it cannot accurately assess visceral fat, has limited ability to differentiate between lean body mass and adiposity, and its correlation with adiposity varies by biological sex, age, race, and disease (25). RFM is an emerging metric used for estimating total body fat percentage based on waist circumference, height, and biological sex. It is also a more accurate estimate of total body fat percentage than BMI (7). Another critical mechanism linking obesity to sleep disorders is dietary (12). Indeed, dietary fatty acids have been reported to be involved in sleep regulation through their dynamic effects on biochemical compounds, including complex lipids, prostaglandins, neurotransmitters, and amino acids, which are essential in initiating and maintaining sleep. Indeed, poor dietary quality (characterized by high-fat and low-fiber intake) has been associated with sleep disorders (26, 27). Notably, in the study conducted by Dragana Rogulja et al., it was observed that peptone supplementation led to an increase in CCHa1 levels within Drosophila gut cells, subsequently facilitating the suppression of sensory arousal during sleep and promoting longer periods of deep sleep among the flies (28). Overindulging in a high-fructose hypercaloric diet has been linked to elevated cortisol excretion in the urine and a greater sleeping metabolic rate when compared to an equivalent overfeeding diet where the carbs originate from whole-wheat items (29). The function of the dietary carbohydrate profile in supporting various hormonal responses to modify nocturnal glucose metabolism may be shown by such clinical findings. Additional research has concentrated on the significance of carbohydrate consumption for the quality of sleep, with mounting data suggesting that the quality of carbohydrates in particular plays a significant role in determining the quality of sleep (30, 31). More specifically, reduced incidence of insomnia and improved quality of sleep have been associated with diets high in fiber and low in glycemic index carbohydrates (32). On the other hand, a higher frequency of sleep problems has been linked to diets heavy in starch, added sugars, and refined grains, or those with a high glycemic index (33, 34). However, whether LCD scores are associated with sleep disorders has not been reported. Our study suggests that RFM and a high-carbohydrate diet have an essential role in developing sleep disorders. Therefore, we believe that RFM and LCD scores are important in diagnosing sleep disorders and facilitating early disease recognition. This study is groundbreaking in examining the relationship between RFM and LCD score and sleep disorders.

The present study has several significant strengths. First, a cross-sectional design was used, employing a broadly representative sample from NHANES (National Health and Nutrition Examination Survey). The large sample size enhances the reliability and representativeness of the study. Second, the study utilized a complex multi-stage probability sampling design with extensive covariate adjustment. This approach improved the precision of statistical inference and provided plausible insights into the relationship between RFM and LCD scores and sleep disorders. Notably, our analyses also incorporated correlation analyses of RFM and LCD scores. Our results suggested that elevated LCD scores led to increased RFM and that those who were inherently obese also had a greater preference for a high-carbohydrate diet. Subsequently, we utilized a variety of regression models to explore the relationship between RFM and LCD scores and sleep disorders. In addition, we further showed that the effects of RFM and LCD scores on sleep disorders were robust by stratifying the analysis across different populations. Finally, we also conducted RCS analysis to explore whether there is a nonlinear relationship between RFM and LCD score and sleep disorders based on regression models incorporating multiple covariates. Our results revealed a significant effect model for the relationship between RFM and LCD score and sleep disorders, although this effect seemed to favor linearity over nonlinearity.

The present study also has some limitations. First, establishing a causal relationship between RFM and LCD scores and sleep disorders is challenging due to the cross-sectional design. Further longitudinal studies are needed to validate these findings. Second, data limitations in the NHANES database may have resulted in incomplete information on specific aspects of psoriasis, such as symptom severity and duration, which may affect the interpretation and accuracy of the results.

In summary, future longitudinal studies and more accurate data collection methods are warranted to more comprehensively explore the clinical manifestations of sleep disorders and their relationship with RFM and LCD scores. Such an exploration could provide more effective strategies for the intervention and prevention of sleep disorders.

In conclusion, our study found that RFM and LCD scores were associated with sleep disorders and explored subgroup differences with nonlinear relationships, thus providing important implications for clinical practice and public health. According to our study, diets that are high in fat and protein have been found to significantly contribute to the accumulation of relative fat mass, thereby playing a crucial role in the development of obesity. Furthermore, it has been observed that individuals with obesity who consume diets rich in fat and protein are at an elevated risk for experiencing sleep disorders. Since obesity with a LCD is a modifiable factor, it is possible to prevent and treat sleep disorders by promoting a healthy diet and appropriate exercise. However, further research is needed to determine whether RFM and LCD scores directly influence sleep disorders.